A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

no code implementations • 16 Jul 2019 • Raimondas Galvelis, Stefan Doerr, Joao M. Damas, Matt J. Harvey, Gianni de Fabritiis

We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2).

Chemical Physics Biological Physics Computational Physics